arXiv research

ProJIVE integrates multiple data types to explain joint and individual variation.

New method calculates geometric Brownian motion with affine drift and its integral.

This is a survey based on joint work with Florian Hanisch and Batu Güneysu reporting on a rigorous construction of the supersymmetric path integral associated to compact spin manifolds.

Anosov diffeomorphisms with integrable subbundles have coherent dynamics and spectral rigidity.

Unified model learns joint and individual features from brain imaging data.

Research in several fields now requires the analysis of data sets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introd…

Kernel method embeds noisy datasets, capturing shared structures.

An important application of Lebesgue integral quadrature arXiv:1807.06007 is developed. Given two random processes, $f(x)$ and $g(x)$, two generalized eigenvalue problems can be formulated and solved. In addition to obtaining two Lebesgue quadratures (for $f$ and $g$) from two eigenproblems, the projections of $f$- and…

We consider an application involving a financial quadratic portfolio of options, when the joint underlying log-returns changes with multivariate elliptic distribution. This motivates the needs for methods for the approximation of multiple integrals over hyperboloids. A transformation is used to reduce the hyperboloid i…

Study improves probabilistic circuits using transformations for better predictions.

Paper uses non-Euclidean analysis to classify brain structure variations.

The paper introduces ESE scores for farmers to assess climate change risks.

VPP learns joint policies for multi-agent RL through interactions.

Derives integral representations for a Lévy process and its extremum, hitting time, with fast evaluation.

This paper analyzes AJIVE for estimating shared subspace across multiple datasets, revealing its strengths and limitations.

Consider the Slepian process $S$ defined by $S(t)=B(t+1)-B(t),t\in [0,1]$ with $B(t),t\in \R$ a standard Brownian motion.In this contribution we analyze the joint distribution between the maximum $m_{s}=\max_{0\leq u\leq s}S(u)$ certain and the maximum $M_t=\max_{0\leq u\leq t}S(u)$ for $0< s < t$ fixed. Explicit inte…

We explain new developments in classical knot theory in 3 and 4~dimensions, i.e. we study knots in 3-space, up to isotopy as well as up to concordance. In dimension~3 we give a geometric interpretation of the Kontsevich integral (joint with Jim Conant), and in dimension 4 we introduce new concordance invariants using v…

Estimates joint probability distribution from 1-way marginals using low-rank tensors and random projections.

Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, resp…

Integrative analysis of disparate data blocks measured on a common set of experimental subjects is a major challenge in modern data analysis. This data structure naturally motivates the simultaneous exploration of the joint and individual variation within each data block resulting in new insights. For instance, there i…

Fast method developed for pricing barrier options and joint Lévy process distributions.

A constructive version of the Frobenius integrability theorem -- that can be programmed effectively -- is given. This is used in computing invariants of groups of low ranks and recover examples from a recent paper of Boyko, Patera and Popoyvich \cite{BPP}.

Perfect pairing for tropical cycles on integral affine manifolds.

New model improves multimodal autoencoders by learning joint and conditional distributions.

Efficiently combines autoregressive and set-based models for joint distributions.

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. A major approach to achieve this objective is to train a model that integrates all the information of different modalities into a joint representation and then t…

The problem of determining the joint probability distributions for correlated random variables with pre-specified marginals is considered. When the joint distribution satisfying all the required conditions is not unique, the "most unbiased" choice corresponds to the distribution of maximum entropy. The calculation of t…

Matrix decomposition is a popular and fundamental approach in machine learning and data mining. It has been successfully applied into various fields. Most matrix decomposition methods focus on decomposing a data matrix from one single source. However, it is common that data are from different sources with heterogeneous…

Credit risk analysis improved with a joint model for spatial and temporal effects.

TransformerLSR models longitudinal, recurrent, and survival data jointly.

Optimal online learning for joint pricing and resource allocation.

This is a continuation of the joint paper with the same title by A.Belenkiy and Yu.Burago. It is proved here that two homeomorphic closed Alexandrov surfaces (of bounded integral curvature) are bi-Lipschitz with a constant depending only on upper bounds of their Euler number, diameters, negative integral curvatures, an…

This study uses Tsallis entropy to analyze diversification and integration in Italian stock market companies.

Proposes a method to integrate learner models robustly against misspecifications.

A new method for aligning datasets without known correspondences.

New method models MTPP without predefined intensity functions.

CoDeQ simplifies joint model compression by integrating pruning and quantization.

Rozansky and Witten proposed in 1996 a family of new three-dimensional topological quantum field theories, indexed by compact (or asymptotically flat) hyperkaehler manifolds. As a byproduct they proved that hyperkaehler manifolds also give rise to Vassiliev weight systems. These may be thought of as invariants of hyper…

This is a substantially revised version. The Kontsevich integral of a knot is a graph-valued invariant which (when graded by the Vassiliev degree of graphs) is characterized by a universal property; namely it is a universal Vassiliev invariant of knots. We introduce a second grading of the Kontsevich integral, the Eule…

This paper proposes a joint energy and data market to handle uncertainty in energy procurement.

Attention-based encoder-decoder neural network models have recently shown promising results in goal-oriented dialogue systems. However, these models struggle to reason over and incorporate state-full knowledge while preserving their end-to-end text generation functionality. Since such models can greatly benefit from us…

Tackling pattern recognition problems in areas such as computer vision, bioinformatics, speech or text recognition is often done best by taking into account task-specific statistical relations between output variables. In structured prediction, this internal structure is used to predict multiple outputs simultaneously,…

Drawdown (resp. drawup) of a stochastic process, also referred as the reflected process at its supremum (resp. infimum), has wide applications in many areas including financial risk management, actuarial mathematics and statistics. In this paper, for general time-homogeneous Markov processes, we study the joint law of …

New approach for open ad hoc teamwork using graph-based policy learning.

Optimal joint separation condition for radar and communications channels in dual-blind deconvolution.

ION-C solves overlapping network integration problems efficiently.

We derive the explicit formula for the joint Laplace transform of the Wishart process and its time integral which extends the original approach of Bru. We compare our methodology with the alternative results given by the variation of constants method, the linearization of the Matrix Riccati ODE's and the Runge-Kutta al…

To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a…